Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Cell Rep. 2018 Nov 6;25(6):1458-1468.e4. doi: 10.1016/j.celrep.2018.10.047.


Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome.

Keywords: cancer patient samples; cell-cell communication; computational analysis; ligand-receptor interaction; single-cell RNA sequencing; syngeneic mouse models; tumor microenvironment.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Cell Communication*
  • Cell Line, Tumor
  • Disease Models, Animal
  • Ligands
  • Melanoma / pathology
  • Mice
  • Neoplasm Metastasis
  • Neoplasms / pathology*
  • Phenotype
  • Receptors, Cell Surface / metabolism
  • Sequence Analysis, RNA*
  • Single-Cell Analysis*
  • Tumor Microenvironment


  • Ligands
  • Receptors, Cell Surface